{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is a Support Vector Machine?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Support Vector Machine (SVM) is a powerful and versatile supervised machine learning model, used for both classification and regression tasks. However, it is mostly used in classification problems.\n",
    "\n",
    "In the SVM algorithm, we plot each data item in the dataset in an n-dimensional space (where n is the number of features you have) with the value of each feature being the value of a particular coordinate. Then, we perform classification by finding the hyperplane that differentiates the two classes very well.\n",
    "\n",
    "Key concepts in SVM:\n",
    "\n",
    "1. **Support Vectors**: These are the data points that are closest to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier.\n",
    "\n",
    "2. **Hyperplane**: In an n-dimensional space, a hyperplane is a subspace of dimension n-1. In SVM, a hyperplane is a line that linearly separates and classifies a set of data.\n",
    "\n",
    "3. **Margin**: The margin is the distance between the hyperplane and the nearest data point from either class. The goal of SVM is to find a hyperplane with the maximum possible margin between the hyperplane and any point within the training set to increase the separation of classes.\n",
    "\n",
    "SVMs can also handle non-linear data by using a kernel trick to transform the input space to a higher dimension, and then finding the hyperplane in this transformed space."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Don’t get confused between SVM and logistic regression. Both the algorithms try to find the best hyperplane, but the main difference is logistic regression is a probabilistic approach whereas support vector machine is based on statistical approaches."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression vs Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a comparison of Logistic Regression and Support Vector Machines (SVM) in a tabular format:\n",
    "\n",
    "| Feature | Logistic Regression | Support Vector Machines (SVM) |\n",
    "|---------|---------------------|-------------------------------|\n",
    "| Decision Boundary | Uses a probabilistic approach, with a linear decision boundary. | Tries to find the hyperplane that maximizes the margin between classes. Can be linear or non-linear (using the kernel trick). |\n",
    "| Handling of Outliers | Can be sensitive to outliers. | Handles outliers better as it tries to maximize the margin and is only influenced by the support vectors. |\n",
    "| Large Datasets | More efficient and easier to implement and update, which can be an advantage with large datasets. | Can be computationally expensive and harder to tune, especially with non-linear kernels. |\n",
    "| Probabilistic Output | Provides a classification output and also gives a probabilistic interpretation by outputting the class probabilities. | Does not directly provide probability estimates. |\n",
    "| Multiclass Classification | Naturally extends to multi-class classification. | Inherently a binary classifier, but multi-class problems can be handled by using strategies like one-vs-one or one-vs-rest. |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Types of Support Vector Machine Algorithms?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Support Vector Machines (SVMs) come in several types, depending on the kind of problem they are used to solve:\n",
    "\n",
    "1. **Linear SVMs**: Linear SVM is the simplest form of SVMs. It is used for binary classification and tries to find a hyperplane that separates the two classes in the best possible way.\n",
    "\n",
    "2. **Non-linear SVMs**: When data is not linearly separable, non-linear SVMs can be used. They use a kernel trick to transform the input space to a higher dimension where a hyperplane can be used to separate the data.\n",
    "\n",
    "3. **One-class SVMs**: This type of SVM is used for novelty detection, i.e., to detect new or unseen data that is not part of the training set. It separates all the data points from the origin in the high dimensional space and maximizes the distance from the origin to the hyperplane.\n",
    "\n",
    "4. **Support Vector Regression (SVR)**: SVR is the application of SVM in regression problems. Instead of trying to find the largest possible margin between classes (as in classification), SVR tries to fit the best line within a predefined or specified boundary called an epsilon.\n",
    "\n",
    "5. **Multiclass SVMs**: SVMs are inherently binary classifiers, but they can be extended to handle multi-class classification problems. There are mainly two methods to do this: one-vs-rest (also known as one-vs-all) and one-vs-one. In one-vs-rest, one class is chosen as the positive class and all other classes are grouped into a single second class. This process is repeated for each class. In one-vs-one, a separate classifier is trained for every pair of classes. The class that gets the most votes is chosen as the final class.\n",
    "\n",
    "Each of these types of SVMs has its own use cases and is used based on the problem at hand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How Does Support Vector Machine Work?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "image/jpeg": {
       "height": 700,
       "width": 700
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='images/SVM.jpg',width=700,height=700)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification or regression problems. Here's a step-by-step explanation of how it works for a binary classification problem:\n",
    "\n",
    "1. **Mapping Data to High-Dimensional Space**: SVM starts by mapping the input data vectors to a high-dimensional feature space using a process called the kernel trick. The kernel function can be linear or non-linear (like Polynomial, Gaussian, Radial Basis Function, etc.), and it's used to compute the dot product of two vectors in the high-dimensional space.\n",
    "\n",
    "2. **Finding the Optimal Hyperplane**: Once the data is in this space, SVM then tries to find the optimal hyperplane that separates the data into two classes. In two dimensions, a hyperplane is a line that linearly separates and classifies a set of data. In three dimensions, it's a plane, and in more dimensions you can call it a hyperplane.\n",
    "\n",
    "3. **Maximizing the Margin**: The optimal hyperplane is the one that maximizes the margin between the two classes. The margin is defined as the distance between the separating hyperplane (decision boundary) and the nearest data point from either class. These nearest points are known as support vectors, as they support the calculation of the decision boundary.\n",
    "\n",
    "4. **Classifying New Data**: Once the optimal hyperplane is found, new data is classified by determining which side of the hyperplane they fall on.\n",
    "\n",
    "In the case of non-linearly separable data, SVM uses a soft margin, allowing some misclassifications in order to achieve a better overall model. The balance between maximizing the margin and minimizing the misclassification is controlled by a parameter often denoted as C. A smaller C creates a wider margin but allows more misclassifications, while a larger C creates a narrower margin but allows fewer misclassifications.\n",
    "\n",
    "It's important to note that while SVM is primarily used for binary classification, it can be extended to multiclass classification, usually by building a binary classifier for each pair of classes and then choosing the class that gets the most votes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mathematical Intuition Behind Support Vector Machine?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Support Vector Machine (SVM) is a powerful machine learning model used for classification or regression. The main idea behind SVM is to find the optimal hyperplane which maximizes the margin between two classes. Here's the mathematical intuition behind it:\n",
    "\n",
    "1. **Hyperplane**: In an n-dimensional space, a hyperplane is a flat subspace of dimension n-1. In a 2D space, the hyperplane is a line, and in a 3D space, it's a plane. The equation of a hyperplane is given by `w.x + b = 0`, where `w` is the weight vector, `x` is the input vector, and `b` is the bias.\n",
    "\n",
    "2. **Margin**: The margin is the distance between the hyperplane (decision boundary) and the closest points from each class, known as support vectors. The goal of SVM is to maximize this margin. The margin `m` can be computed as `m = 2 / ||w||`, where `||w||` is the Euclidean norm (length) of the weight vector `w`. So, maximizing `m` is equivalent to minimizing `||w||`.\n",
    "\n",
    "3. **Constraint Optimization Problem**: The SVM problem can be formulated as a constraint optimization problem: Minimize `1/2 * ||w||^2` subject to the constraints `y(i) * (w.x(i) + b) >= 1` for all `i`, where `y(i)` is the class label (-1 or 1) of the data point `x(i)`. This is a quadratic programming problem.\n",
    "\n",
    "4. **Lagrange Multipliers**: To solve the constraint optimization problem, we use a method from calculus called Lagrange multipliers. This introduces a Lagrange multiplier `α(i)` for each constraint, and we solve for `w` and `b` that maximize the Lagrangian dual function.\n",
    "\n",
    "5. **Kernel Trick**: For non-linearly separable data, SVM uses a trick called the kernel trick. It maps the input vectors into a higher-dimensional space where they become linearly separable. The kernel function `K(x, y) = φ(x).φ(y)` computes the dot product in the high-dimensional space without having to explicitly compute the transformation φ.\n",
    "\n",
    "6. **Soft Margin and Regularization**: In practice, data is often not perfectly separable, so SVM allows some misclassifications, introducing a slack variable ξ and a regularization parameter C. The new optimization problem becomes: Minimize `1/2 * ||w||^2 + C * Σ ξ(i)` subject to `y(i) * (w.x(i) + b) >= 1 - ξ(i)` for all `i`.\n",
    "\n",
    "This is a high-level overview of the mathematics behind SVM. The actual details involve more complex mathematical concepts from linear algebra, calculus, and optimization theory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now the question comes\n",
    "1. Why the magnitude is equal, why didn’t we take 1 and -2?\n",
    "2. Why did we only take 1 and -1, why not any other value like 24 and -100?\n",
    "3. Why did we assume this line?\n",
    "\n",
    "* Let’s try to answer these questions\n",
    "1. We want our plane to have equal distance from both the classes that means L should pass through the center of L1 and L2 that’s why we take magnitude equal.\n",
    "2. Let’s say the equation of our hyperplane is 2x+y=2, we observe that even if we multiply the whole equation with some other number the line doesn’t change (try plotting on a graph). Hence for mathematical convenience, we take it as 1.\n",
    "3. Now the main question is exactly why there’s a need to assume only this line? To answer this, I’ll try to take the help of graphs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kernels in Support Vector Machine?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='images/K.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Support Vector Machines (SVM), a kernel is a function that is used to transform the data into a higher-dimensional space where it becomes easier to find a hyperplane that separates the data points into different classes. This is known as the kernel trick, and it allows SVM to solve non-linear classification problems.\n",
    "\n",
    "There are several types of kernels used in SVM:\n",
    "\n",
    "1. **Linear Kernel**: The linear kernel is the simplest kernel function. It is given by the inner product `<x, y> + c` where `c` is a constant. When using a linear kernel, SVM tries to separate the data with a straight line (or a hyperplane in higher dimensions).\n",
    "\n",
    "2. **Polynomial Kernel**: The polynomial kernel allows SVM to classify data that is separable by a polynomial decision boundary. The polynomial kernel is given by `(γ<x, y> + r)^d` where `γ`, `r`, and `d` are kernel parameters, and `d` is the degree of the polynomial.\n",
    "\n",
    "3. **Radial Basis Function (RBF) or Gaussian Kernel**: The RBF kernel allows SVM to classify data that is separable by a non-linear decision boundary. It is given by `exp(-γ||x - y||^2)`, where `γ` is a kernel parameter. The RBF kernel maps input vectors into an infinite-dimensional space, making it a popular choice for many different types of data.\n",
    "\n",
    "4. **Sigmoid Kernel**: The sigmoid kernel is given by `tanh(γ<x, y> + r)`, where `γ` and `r` are kernel parameters. The sigmoid kernel has properties similar to the logistic sigmoid function and is often used in neural networks.\n",
    "\n",
    "Choosing the right kernel and the right kernel parameters is crucial for the performance of SVM. The choice depends on the data and the problem at hand. Cross-validation is often used to select the best kernel and kernel parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to Choose the Right Kernel? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choosing the right kernel for a Support Vector Machine (SVM) depends on the data and the problem at hand. Here are some general guidelines:\n",
    "\n",
    "1. **Linear Kernel**: If the number of features is large compared to the number of observations, or if it's clear that the decision boundary is linear, then using a linear kernel (or even linear models like logistic regression) can be a good choice.\n",
    "\n",
    "2. **Polynomial Kernel**: Polynomial kernels are useful when the data is nearly linearly separable and the problem requires a more complex model to avoid underfitting. The degree of the polynomial determines the complexity of the decision boundary. However, high degrees can lead to overfitting, so it's important to use cross-validation to choose the best degree.\n",
    "\n",
    "3. **Radial Basis Function (RBF) or Gaussian Kernel**: The RBF kernel is a good choice when the number of observations is large compared to the number of features, or when there is no prior knowledge about the data. It can handle a wide range of situations and is often a safe first choice.\n",
    "\n",
    "4. **Sigmoid Kernel**: The sigmoid kernel is mainly used in neural networks and is less common in SVMs. It can be used as a proxy for neural networks in some cases.\n",
    "\n",
    "In addition to choosing the kernel, it's also important to tune the kernel parameters. This can be done using techniques like grid search or random search combined with cross-validation.\n",
    "\n",
    "Remember, there is no one-size-fits-all kernel for SVM. It's always a good idea to try different kernels and kernel parameters and see which one works best for your specific problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code Implementation in Python:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a simple example of how to use the Support Vector Machine (SVM) classifier from the scikit-learn library in Python. We'll use the Iris dataset, which is a multivariate dataset included in scikit-learn's datasets module.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As a Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy:  1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Load the iris dataset\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "# Split the dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create a SVM classifier with a linear kernel\n",
    "clf = svm.SVC(kernel='linear')\n",
    "\n",
    "# Train the classifier\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Predict the response for the test dataset\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy of our model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print('Model Accuracy: ', accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "This code first loads the Iris dataset and splits it into a training set and a test set. Then it creates an SVM classifier with a linear kernel, trains the classifier on the training data, and makes predictions on the test data. Finally, it calculates the accuracy of the model by comparing the predicted labels with the true labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As a Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use Support Vector Machines for regression tasks as well. This is known as Support Vector Regression (SVR). Here's how you can modify the previous code to perform regression on the Iris dataset using SVR:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error:  0.1918736465878495\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Load the iris dataset\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "# We'll use only the first feature for simplicity\n",
    "X = iris.data[:, :1]\n",
    "y = iris.target\n",
    "\n",
    "# Split the dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create a SVR with a linear kernel\n",
    "regressor = svm.SVR(kernel='linear')\n",
    "\n",
    "# Train the regressor\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Predict the response for the test dataset\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "# Calculate the mean squared error of our model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "print('Mean Squared Error: ', mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "This code is very similar to the previous one, but instead of using `svm.SVC` for classification, it uses `svm.SVR` for regression. Also, instead of calculating the accuracy of the model, it calculates the mean squared error, which is a common metric for regression tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code Implementation In R:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As a Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's how you can implement a Support Vector Machine (SVM) classifier in R using the `e1071` package. We'll use the iris dataset, which is built into R.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: e1071\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Model Accuracy:  0.966666666666667\"\n"
     ]
    }
   ],
   "source": [
    "# Install and load the e1071 package\n",
    "if (!require(e1071)) {\n",
    "  install.packages(\"e1071\")\n",
    "  library(e1071)\n",
    "}\n",
    "\n",
    "# Load the iris dataset\n",
    "data(iris)\n",
    "\n",
    "# Split the dataset into training set and test set\n",
    "set.seed(42)\n",
    "train_index <- sample(1:nrow(iris), nrow(iris)*0.8)\n",
    "train_set <- iris[train_index, ]\n",
    "test_set <- iris[-train_index, ]\n",
    "\n",
    "# Create a SVM classifier with a linear kernel\n",
    "svm_model <- svm(Species ~ ., data = train_set, kernel = \"linear\")\n",
    "\n",
    "# Predict the response for the test dataset\n",
    "predictions <- predict(svm_model, test_set)\n",
    "\n",
    "# Calculate the accuracy of our model\n",
    "accuracy <- sum(predictions == test_set$Species) / nrow(test_set)\n",
    "\n",
    "print(paste('Model Accuracy: ', accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "This code first installs and loads the `e1071` package, which provides the `svm` function for training a SVM classifier. Then it loads the iris dataset and splits it into a training set and a test set. It trains the SVM classifier on the training data and makes predictions on the test data. Finally, it calculates the accuracy of the model by comparing the predicted labels with the true labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As a Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's how you can implement a Support Vector Regression (SVR) in R using the `e1071` package. We'll use the mtcars dataset, which is built into R.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Mean Squared Error:  17.9412113862077\"\n"
     ]
    }
   ],
   "source": [
    "# Install and load the e1071 package\n",
    "if (!require(e1071)) {\n",
    "  install.packages(\"e1071\")\n",
    "  library(e1071)\n",
    "}\n",
    "\n",
    "# Load the mtcars dataset\n",
    "data(mtcars)\n",
    "\n",
    "# We'll predict mpg based on other variables\n",
    "# Split the dataset into training set and test set\n",
    "set.seed(42)\n",
    "train_index <- sample(1:nrow(mtcars), nrow(mtcars)*0.8)\n",
    "train_set <- mtcars[train_index, ]\n",
    "test_set <- mtcars[-train_index, ]\n",
    "\n",
    "# Create a SVR with a linear kernel\n",
    "svr_model <- svm(mpg ~ ., data = train_set, kernel = \"linear\")\n",
    "\n",
    "# Predict the response for the test dataset\n",
    "predictions <- predict(svr_model, test_set)\n",
    "\n",
    "# Calculate the mean squared error of our model\n",
    "mse <- mean((predictions - test_set$mpg)^2)\n",
    "\n",
    "print(paste('Mean Squared Error: ', mse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "This code is very similar to the previous one, but instead of using `svm` for classification, it uses `svm` for regression. Also, instead of calculating the accuracy of the model, it calculates the mean squared error, which is a common metric for regression tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are the advantages and disadvantages of using SVM compared to other classification algorithms?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Support Vector Machines (SVM) have several advantages and disadvantages when compared to other classification algorithms:\n",
    "\n",
    "**Advantages:**\n",
    "\n",
    "1. **Effective in high-dimensional spaces**: SVMs are effective when the number of features is large relative to the number of observations.\n",
    "\n",
    "2. **Memory efficient**: SVMs use a subset of training points (support vectors) in the decision function, which makes it memory efficient.\n",
    "\n",
    "3. **Versatile**: Different kernel functions can be specified for the decision function, which makes SVM versatile for various types of data.\n",
    "\n",
    "**Disadvantages:**\n",
    "\n",
    "1. **Poor performance when number of features exceeds number of samples**: SVMs do not perform well when the number of features exceeds the number of samples.\n",
    "\n",
    "2. **No probability estimates**: SVMs do not directly provide probability estimates. These are calculated using an expensive five-fold cross-validation.\n",
    "\n",
    "3. **Sensitive to noise**: A relatively small number of mislabeled examples can dramatically decrease the performance.\n",
    "\n",
    "4. **Complex parameters**: The complexity of choosing the right kernel and the right kernel parameters can be a disadvantage.\n",
    "\n",
    "Remember, the choice of algorithm depends on the specific problem and the dataset at hand. It's always a good idea to try several different algorithms and see which one works best for your specific problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Referecnce: \n",
    "1. Analytics Vidya"
   ]
  }
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